Three-stage feature fusion rotary machinery fault diagnosis method based on multi-modal data

A feature fusion, rotating machinery technology, applied in the testing of mechanical parts, computer parts, character and pattern recognition, etc., can solve the problems of unstable classification results and strong feature selection dependence.

Active Publication Date: 2021-06-08
SHANDONG UNIV
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  • Abstract
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Problems solved by technology

However, despite the success of these intelligent methods, there are still two disadvantages: (1) These intelligent fault diagnosis methods need to be combined with feature extraction methods, resulting in a strong dependence on f

Method used

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  • Three-stage feature fusion rotary machinery fault diagnosis method based on multi-modal data
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  • Three-stage feature fusion rotary machinery fault diagnosis method based on multi-modal data

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Embodiment 1

[0035] Embodiment 1 of the present disclosure provides a three-stage feature fusion rotating machinery fault diagnosis method based on multimodal data, which is characterized in that it includes the following steps:

[0036] Obtain the parameter data of the mechanical operation state, and obtain the data of at least two modes;

[0037] Input the obtained modal data into the preset neural network model respectively to obtain the final fault classification result;

[0038] Wherein, the preset neural network model at least sequentially includes three stages of first self-feature fusion, mutual feature fusion and second self-feature fusion.

[0039] Current research mainly focuses on fusing the last layer of features from multimodal data. With the deepening of the feature layer, part of the fault information will be lost, and the fusion of the last layer of features may not be the best choice. In response to this problem, this embodiment uses the vibration and torque signal data...

Embodiment 2

[0075] Embodiment 2 of the present disclosure provides a three-stage feature fusion rotating machinery fault diagnosis system based on multimodal data, including:

[0076] The data acquisition module is configured to: acquire mechanical operating state parameter data, and obtain data of at least two modes;

[0077] The fault classification module is configured to: respectively input the acquired modal data into a preset neural network model to obtain a final fault classification result;

[0078] Wherein, the preset neural network model at least sequentially includes three stages of first self-feature fusion, mutual feature fusion and second self-feature fusion.

[0079] The working method of the system is the same as the three-stage feature fusion rotating machinery fault diagnosis method based on multimodal data provided in Embodiment 1, and will not be repeated here.

Embodiment 3

[0081] Embodiment 3 of the present disclosure provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the three-stage feature fusion rotating machine based on multimodal data as described in Embodiment 1 of the present disclosure is implemented. The steps in the fault diagnosis method, the steps are:

[0082] Obtain the parameter data of the mechanical operation state, and obtain the data of at least two modes;

[0083] Input the obtained modal data into the preset neural network model respectively to obtain the final fault classification result;

[0084] Wherein, the preset neural network model at least sequentially includes three stages of first self-feature fusion, mutual feature fusion and second self-feature fusion.

[0085] The detailed steps are the same as the multimodal data-based three-stage feature fusion rotating machinery fault diagnosis method provided in Embodiment 1, and will not be repeated here. ...

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Abstract

The invention provides a three-stage feature fusion rotary machine fault diagnosis method based on multi-modal data. The method comprises the steps: obtaining the operation state parameter data of a machine, and obtaining the data of at least two modals; inputting the obtained modal data into a preset neural network model to obtain a final fault classification result; wherein the preset neural network model at least sequentially comprises three stages of first self-feature fusion, mutual feature fusion and second self-feature fusion; according to the method, a three-stage feature fusion method including a one-dimensional convolutional neural network and a two-dimensional convolutional neural network is adopted, multi-scale features are fused and fault diagnosis is carried out, the two-dimensional convolutional neural network extracts correlation between feature mapping maps, an attention mechanism can carry out different weight distribution on the feature maps, and the fault diagnosis accuracy is improved. Important information is highlighted, redundant information is reduced, and the fault diagnosis performance is greatly improved.

Description

technical field [0001] The present disclosure relates to the technical field of mechanical fault diagnosis, in particular to a three-stage feature fusion method for fault diagnosis of rotating machinery based on multimodal data. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] With the continuous development of intelligent manufacturing, industrial systems are becoming more and more complex and non-linear, and the losses caused by equipment damage are also increasing. Early fault detection can not only eliminate faults before they cause huge economic losses, but also avoid major safety accidents. However, due to the complexity and nonlinearity of industrial systems, it is difficult to build an accurate model. Due to the continuous development of information science and technology, industrial systems have produced a large amount of opera...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G01M13/045
CPCG01M13/00G06N3/045G06F18/241G06F18/253
Inventor 李沂滨王代超贾磊宋艳高晟耀
Owner SHANDONG UNIV
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